The present invention relates generally to a technology of a multi-spectral image photographed by multi-band photography and more particularly, to a method of compressing a multi-spectral image which makes full use of characteristics thereof.
A color image photographed by a digital camera and a color image photographed by a conventional camera or otherwise are reproduced and analyzed normally by use of images (image data) based on three channels of R (red), G (green) and B (blue).
A color image (a photographing scene), however, contains so many contents of information, and hence there might often arise a case of being unable to obtain sufficient contents of information from the images based on three channels of R, G and B when analyzing spectral sensitivity characteristics of the digital camera, a photosensitive material, etc. at a high accuracy, and analyzing and reproducing the images at the high accuracy.
A multi-spectral image photographed by multi-band photography is known as an image in which the above problem can be obviated.
The multi-spectral image comprises images based on multi-channels having different spectral distribution ranges. A formation of the multi-spectral image involves the use of over four types, e.g., six types of filters having spectral transmittance characteristics of which peak wavelengths are respectively 450 nm, 500 nm, 550 nm, 600 nm, 650 nm and 700 nm. Those filters are inserted in sequence, and the same object (scene) is photographed the same number of times as the number of filters, thereby making it feasible to obtain the multi-spectral image comprising the plurality of images having the spectral distribution ranges different from each other corresponding to the number of filters for use.
This method is capable of obtaining the image in which wavelength ranges of the information contained in the object are segmented corresponding to the number of filters for use and wavelength resolutions, and hence it is possible to perform analyses of a digital camera, a photosensitive material, etc., and analyses, reproduction, etc. of the images at a high precision.
The multi-spectral image, however, normally includes images based on four to sixteen channels, and therefore has by far a larger quantity of image data than the conventional images based on three channels of R, G and B. A large storage region, volume or capacity is needed for handling the data, and besides reading and writing processes of the data require a comparatively long period of time.
Further, it is required that a whole image be observed (reproduced) in order to grasp states, patterns, etc. of the images, an operation which takes much time and labor.
It is a primary object of the present invention, which was devised to obviate the problems inherent in the prior art described above, to provide a method of compressing a multi-spectral image, which is capable of preferably compressing the multi-spectral image while making use of characteristics thereof and grasping an entire configuration of the image without a necessity for observing all elemental images.
To accomplish the above object, according to one aspect of the present invention, a method of compressing a multi-spectral image formed of a plurality of elemental images having different spectral distribution ranges, comprising a step of extracting pairs of elemental images having respective spectral distribution ranges close to each other from the plurality of elemental images, a step of averaging the thus extracted pairs of elemental images to calculate average images, provided that an elemental image that does not form a pair is taken to be as such an average image, a step of further extracting pairs of inferior average images having respective spectral distribution ranges close to each other, a step of further averaging the thus extracted pairs of inferior average images to calculate superior average images, provided that an inferior average image that does not form a pair is taken to be as such a superior average image, a step of hierarchically repeating the averaging steps in which the same elemental images and inferior average images are not used to calculate the average images and the superior average images, respectively, thereby finally calculating a superlative average image of all the elemental images, a step of creating differential images obtained by taking differences between all the elemental images and the respective average images and between the inferior average images and the respective superior average, a step of calculating compressed differential images by compressing the differential images and a step of recording the superlative average image and the compressed differential images so as to take a hierarchical structure with the superlative average image being positioned at a top corresponding to a hierarchical structure constructed of all the elemental images, all the average images and the superlative average image positioned at the top.
It is preferable that the two elemental images having the respective spectral distribution ranges close to each other and for calculating the average image are two elemental images having the respective spectral distribution ranges closest to each other and wherein the two inferior average images having the respective spectral distribution ranges close to each other and for calculating the superior average image are two inferior average images having the respective spectral distribution ranges closest to each other.
It is also preferable that the differential images are obtained by taking the differences between the average image and the two elemental images used for calculating the average image, between the superior average image and the two inferior images used for calculating the superior average image and between the superlative average image and the two inferior average images used for calculating the superlative average image.
It is further preferable that the differential images are compressed by using a loss-less compression method to calculate the compressed differential images.
It is still further preferable that the differential images are compressed by using a run length method or a Huffman method to calculate the compressed differential images.
It is another preferable that, in addition to the method described above, the method further comprises a step of calculating a compressed superlative average image by compressing the superlative average image and wherein the compressed superlative average image is recorded in stead of the superlative average image.
It is further preferable that the superlative average image is compressed by using a loss-less compression method to calculate the compressed superlative average image.
It is still further preferable that the superlative average image is compressed by using a run length method or a Huffman method to calculate the compressed superlative average images.
According to the another aspect of the present invention, a method of compressing a multi-spectral image formed of a plurality of elemental images having different spectral distribution ranges, comprising a step of calculating an average image by averaging two elemental images having respective spectral distribution ranges close to each other and by taking an elemental image that does not form a pair to be as such the average image as well as differential images by taking differences between the respective two elemental images and the average image thereof, a step of further calculating a superior average image by averaging two inferior average images having respective spectral distribution ranges close to each other and by taking an inferior average image that does not form a pair to be as such the superior average image as well as differential images by taking differences between the respective two inferior average images and the superior average image thereof, a step of calculating more superior average images and a superlative average image of all the elemental images as well as differential images between respective two more inferior average images and the more superior average image thereof and between the respective two more inferior average images and the superlative average image thereof by hierarchically repeating the further calculating step, a step of calculating compressed differential images by compressing the differential images and a step of recording the superlative average image and the compressed differential images so as to take a hierarchical structure with the superlative average image being positioned at a top corresponding to a hierarchical structure constructed of all the elemental images, all the average images and the superlative average image positioned at the top.